The document summarizes research using the UrbanSim simulation tool to analyze and predict household location choice in the Grand Lyon region of France. Key findings include: 1) UrbanSim was able to reasonably predict the spatial distribution of population in 2005 given 1999 data, though it over-predicted central areas and under-predicted outer suburbs. 2) The most influential factors in household location choice were number of households/units, low population density, and vacancy rates. 3) Different income groups favored locations based on attributes like accessibility, proximity to others of the same income, and housing prices.

1. LET, Transport Economics Laboratory (CNRS, University of Lyon, ENTPE) International Conference of Territorial Intelligence University of Salerno, 4-7 November 2009 Marko Kryvobokov, Nicolas Ovtracht, Val é rie Thiebaut Analysis and prediction of household location choice in Grand Lyon with urban land use simulation tool UrbanSim

7. 1. Introduction MOSART: Numerical Platform of Modelling Mod é lisation et Simulation de l'Accessibilité aux Réseaux et aux Territoires (Modelling and Simulation of Accessibility to Networks and Territories)

10. UrbanSim Model Components and Data Flow (from UrbanSim Project, 2008) 2. UrbanSim

17. 3. Data Travel times from transportation model from MOSART Road network (NAVTEQ) : More than 222000 street segments More than 90000 nodes Public Transport network : More than 2300 stops Bus: more than 100 lines 4 lines of tramway 2 lines of funiculaire 4 lines of metro Regional train: more than 10 lines

19. 4. Model estimation Likelihood ratio test: 4828 Number of observations: 49512 12.79 <0.001 Index of population access if household does not have a car 12 -4.77 -0.171 Log of travel time to the CBD if household does not have a car 11 46.44 1.291 Log of number of households 10 -5.28 <-0.001 Population density 9 13.78 0.025 Percent of low income households wwd if low income household 8 11.67 0.028 Percent of middle income households wwd if middle income household 7 19.49 0.073 Percent of high income households wwd if high income household 6 -2.06 -0.009 Log of residential vacancy rate 5 -45.70 -1.275 Log of number of residential units 4 -9.72 -0.310 Log of average real estate price if low income household 3 -3.38 -0.081 Log of average real estate price if middle income household 2 3.22 0.173 Log of average real estate price if high income household 1 t-value Coefficient Variable Number

20. 5. Simulation results Relative differences in simulated and actual population 2005

21. 5. Simulation results Relative differences between simulated and actual population 2005 53 36 ±20 30 20 ±10 18 12 ±5 Percent of population Percent of ILOTs Relative difference, %

22. 5. Simulation results Differences in simulated and actual population 2005

23. 5. Simulation results Differences in simulated and actual population 2005 in Lyon and Villeurbanne Difference Relative difference

24. 6. Conclusion General tendencies: - The most significant attributes are the number of households and residential units - Low population density and vacancy rate are preferable Different behaviour of social groups: - Accessibility by public transport to the CBD and to population are especially important for households without a car - All income groups strongly prefer location closer to their income group - High income group prefers locations with expensive accommodation - Middle and low income households choose less expensive locations